Python is a crucial language for Data Engineers, widely used in data processing, ETL workflows, and big data frameworks like Apache Spark. In this article, we will cover some commonly asked Python interview questions that will help you prepare for your Data Engineering interviews.

1) What is the Difference Between a Shallow Copy and a Deep Copy?
2) What are Pickling and Unpickling?
3) Are Arguments in Python Passed by Value or by Reference?
4) What Does the ‘is’ Operator Do?
5) What Is the Purpose of the Pass Statement?
6) How Will You Check If All the Characters in a String Are Alphanumeric?
7) What Is the Difference Between Del and Remove() on Lists?
8) Differentiate Between append() and extend() method of List?
9) What Is the Difference Between a List and a Tuple?
10) What Is Docstring in Python?
11) Is Python Object-oriented or Functional Programming?
12) Write a Function Prototype That Takes a Variable Number of Arguments.
13) What Are *args and *kwargs?
14) “In Python, Functions Are First-class Objects.” What Do You Infer from This?
15) What Is the Output Of: Print(__name__)? Justify Your Answer.
16) What are decorators in Python?
17) Differentiate between .pyc and .py. ?
18) Explain global variables and local variables in Python?
19) Is Python case-sensitive?
20) What is the use of self in Python?
21) What are Python modules? Name a few Python built-in modules that are often used?
22) What is _init_?
23) What is the Lambda function?
24) How does continue, break, and pass work?
25) What are Python iterators?
26) What are generators in Python?
27) How do you copy an object in Python?
28) Explain join() and split() functions in Python.
29) Explain polymorphism in Python.
30) What is encapsulation in Python?
31) Are access specifiers used in Python?
32) What is GIL?
33) What is PIP?

These are some of the most common Python interview questions for Data Engineers. Preparing for these topics will help you build confidence and perform well in your interviews.